Numerical optimization algorithms are already established tools to increase and stabilize the
performance of particle accelerators. These algorithms have many advantages, are …

Increasing complexity of modern laser systems, mostly originated from the nonlinear
dynamics of radiation, makes control of their operation more and more challenging, calling …

Optimal tuning of particle accelerators is a challenging task. Many different approaches have
been proposed in the past to solve two main problems—attainment of an optimal working …

Machine learning (ML) is growing in popularity for various particle accelerator applications
including anomaly detection such as faulty beam position monitor or RF fault identification …

In this article we report on the application of a model-free reinforcement learning method to
the optimization of accelerator systems. We simplify a policy gradient algorithm to …

Reinforcement learning holds tremendous promise in accelerator controls. The primary goal
of this paper is to show how this approach can be utilised on an operational level on …

Reinforcement learning algorithms have risen in popularity in the accelerator physics
community in recent years, showing potential in beam control and in the optimization and …

Recently, reinforcement learning (RL) algorithms have been applied to a wide range of
control problems in accelerator commissioning. In order to achieve efficient and fast control …

Beam tuning in particle accelerators presents a significant challenge, especially when the
accelerator's configuration cannot be determined through physical modeling. A common …

The superconducting linear accelerator is a highly flexiable facility for modern scientific
discoveries, necessitating weekly reconfiguration and tuning. Accordingly, minimizing setup …